This master thesis looks at how clustering techniques can be appliedto a collection of scientific documents. Approximately one year of serverlogs from the CERN Document Server (CDS) are analyzed and preprocessed.Based on the findings of this analysis, and a review of thecurrent state of the art, three different clustering methods are selectedfor further work: Simple k-Means, Hierarchical Agglomerative Clustering(HAC) and Graph Partitioning. In addition, a custom, agglomerativeclustering algorithm is made in an attempt to tackle some of the problemsencountered during the experiments with k-Means and HAC. The resultsfrom k-Means and HAC are poor, but the graph partitioning methodyields some promising results.The main conclusion of this thesis is that the inherent clusters withinthe user-record relationship of a scientific collection are nebulous, butexisting. Furthermore, the most common clustering algorithms are notsuitable for this type of clustering.